nvidia-nccl-cu13
NVIDIA Collective Communication Library (NCCL) Runtime
Decision gist · record as of 2026-08-14
Yes—if you are running distributed GPU workloads on CUDA 13 hardware. This is a foundational runtime library for multi-GPU training and inference. However, verify that your framework (PyTorch, TensorFlow, etc.) declares it as a dependency rather than installing it standalone. Unclear license terms warrant review before commercial use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA GPU hardware and CUDA 13 runtime environment; wheels are manylinux_2_18 (glibc 2.18+) on x86_64 or aarch64 only.
- Medium install friction due to platform-specific wheels (aarch64 and x86_64 manylinux).
- Recently released (3 days old) with active maintenance status.
License · maintenance · safety
(unclear) — License treatment is unclear—no SPDX identifier or raw license text provided. Verify licensing terms before use in proprietary or redistributed projects.
last release 2026-08-11 (3 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 43,044,472 downloads/mo, #649 on PyPI
Alternatives
Verify before relying
pip install nvidia-nccl-cu13==2.31.2
import nvidia.nccl
# NCCL is typically used via higher-level frameworks like PyTorch or TensorFlow distributed training- Whether this package is intended for direct use or only as a transitive dependency of higher-level frameworks.
- Exact license terms and any restrictions on commercial or redistributed use.
- Compatibility with specific CUDA 13 minor versions and GPU architectures.
What it is and what it does
NCCL is NVIDIA's low-level communication library for coordinating collective operations across multiple GPUs in a single machine or across a cluster. It implements standard patterns like all-reduce, all-gather, reduce, broadcast, and reduce-scatter, optimized for high bandwidth over PCIe, NVLink, NVswitch, InfiniBand, or standard TCP/IP networking.
This package (nvidia-nccl-cu13) bundles the NCCL runtime for CUDA 13. It is typically not used directly by application code but rather as a dependency of distributed training frameworks like PyTorch or TensorFlow. The package has no Python runtime dependencies and is platform-specific, with wheels built for Linux x86_64 and aarch64 architectures only.
Use it for
- Enable multi-GPU training in PyTorch or TensorFlow by providing the underlying collective communication primitives.
- Accelerate distributed inference across multiple GPUs using optimized all-reduce and all-gather operations.
- Support custom GPU communication patterns in research or production systems that call NCCL directly via C/C++ bindings.
- Provide efficient reduce-scatter for gradient aggregation in data-parallel training across GPU clusters.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes—if you are running distributed GPU workloads on CUDA 13 hardware.
This is a foundational runtime library for multi-GPU training and inference. However, verify that your framework (PyTorch, TensorFlow, etc.) declares it as a dependency rather than installing it standalone. Unclear license terms warrant review before commercial use.
Install
nvidia-nccl-cu13 on PyPI
Before you install
Medium install friction due to platform-specific wheels (aarch64 and x86_64 manylinux). Recently released (3 days old) with active maintenance status. No runtime dependencies to manage.
Requires NVIDIA GPU hardware and CUDA 13 runtime environment; wheels are manylinux_2_18 (glibc 2.18+) on x86_64 or aarch64 only.
License in practice
License treatment is unclear—no SPDX identifier or raw license text provided. Verify licensing terms before use in proprietary or redistributed projects.
Quickstart
pip install nvidia-nccl-cu13==2.31.2
import nvidia.nccl
# NCCL is typically used via higher-level frameworks like PyTorch or TensorFlow distributed trainingVerify before relying
- Whether this package is intended for direct use or only as a transitive dependency of higher-level frameworks.
- Exact license terms and any restrictions on commercial or redistributed use.
- Compatibility with specific CUDA 13 minor versions and GPU architectures.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 3 days since the last release |
| First released | |
| Downloads | 43,044,472 / month, #649 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchNatural Language :: EnglishOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: Libraries |
Evidence: nvidia_nccl_cu13-2.31.2-py3-none-manylinux_2_18_aarch64.whl; nvidia_nccl_cu13-2.31.2-py3-none-manylinux_2_18_x86_64.whl
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See also nccl4py · nvidia-nccl-cu11 · nvidia-nccl-cu12 · nvidia-nvshmem-cu13 · distributed-ucxx-cu12 · nvidia-nvshmem-cu12 · nvidia-cuda-cccl · clusterscope · nvshmem4py-cu13 · lcm